R Notebook

install.packages(c('tsibble', 'tsibbledata', 'feasts'))

also installing the dependencies ‘numDeriv’, ‘distributional’, ‘progressr’, ‘warp’, ‘fabletools’, ‘slider’

  There is a binary version available but the source version is later:

trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.2/numDeriv_2016.8-1.1.tgz'
Content type 'application/x-gzip' length 113250 bytes (110 KB)
==================================================
downloaded 110 KB

trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.2/distributional_0.3.0.tgz'
Content type 'application/x-gzip' length 533681 bytes (521 KB)
==================================================
downloaded 521 KB

trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.2/progressr_0.10.1.tgz'
Content type 'application/x-gzip' length 265811 bytes (259 KB)
==================================================
downloaded 259 KB

trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.2/warp_0.2.0.tgz'
Content type 'application/x-gzip' length 103499 bytes (101 KB)
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downloaded 101 KB

trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.2/fabletools_0.3.2.tgz'
Content type 'application/x-gzip' length 655065 bytes (639 KB)
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downloaded 639 KB

trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.2/slider_0.2.2.tgz'
Content type 'application/x-gzip' length 360768 bytes (352 KB)
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downloaded 352 KB

trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.2/tsibbledata_0.4.0.tgz'
Content type 'application/x-gzip' length 2173335 bytes (2.1 MB)
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downloaded 2.1 MB

trying URL 'https://cran.rstudio.com/bin/macosx/contrib/4.2/feasts_0.2.2.tgz'
Content type 'application/x-gzip' length 1252288 bytes (1.2 MB)
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downloaded 1.2 MB

The downloaded binary packages are in
    /var/folders/h4/vy1nwyvd5kz_n6950nl29pj80000gn/T//RtmpInwGKv/downloaded_packages

installing the source package ‘tsibble’

trying URL 'https://cran.rstudio.com/src/contrib/tsibble_1.1.2.tar.gz'
Content type 'application/x-gzip' length 1520385 bytes (1.4 MB)
==================================================
downloaded 1.4 MB

* installing *source* package ‘tsibble’ ...
** package ‘tsibble’ successfully unpacked and MD5 sums checked
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
*** copying figures
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (tsibble)

The downloaded source packages are in
    ‘/private/var/folders/h4/vy1nwyvd5kz_n6950nl29pj80000gn/T/RtmpInwGKv/downloaded_packages’

library(tsibble)

Attaching package: ‘tsibble’

The following objects are masked from ‘package:base’:

    intersect, setdiff, union

library(tsibbledata)

##tsibble

index : time component key: identifies the underlying structure of the data : in concojunction with the index uniquelu identifies each individual row

index_var(vic_elec)

[1] "Time"

interval(vic_elec)

<interval[1]>
[1] 30m

vic_elec %>% 
  filter(Time < as.POSIXct('2012-01-01 01:00:00', tz = 'Australia/Melbourne'))

vic_elec <- vic_elec %>% 
  mutate(year = lubridate::year(Date))

vic_elec

vic_elec %>% 
  select(year, Temperature) %>% 
  index_by(year) %>% 
  summarise(mean_temp = mean(Temperature))

vic_elec %>% 
  filter(!Holiday) %>% 
  ggplot()+
  geom_line(aes(x=Time, y = Temperature))

elec_date <- vic_elec %>%
  index_by(date = as_date(Time)) %>%
  summarise(temp_mean = mean(Temperature, na.rm = TRUE))

elec_date

floor_date()
ceiling_date()
yearweek()
yearquarter()

elec_date %>% 
  ggplot(aes(date, temp_mean))+
  geom_line()

NA

elec_m <- vic_elec %>% 
  index_by(month = month(Time, label = TRUE)) %>% 
  summarise(temp_mean = mean(Temperature, na.rm = TRUE))
elec_m

elec_m %>% 
  ggplot(aes(month, temp_mean))+
  geom_point()+
  geom_line(group = 1)

elec_year <- vic_elec %>%
  index_by(year = year(Time)) %>%
  summarise(temp_mean = mean(Temperature, na.rm = TRUE))


elec_year %>%
  ggplot(aes(x = year, y = temp_mean)) +
  geom_col(fill = "steelblue", alpha = 0.7) + 
  ylab("Mean Temperature")+ 
  xlab("year")

library(slider)

elec_rolling <- vic_elec %>% 
  mutate(
    temp_moving_avg = slide_dbl(
      .x = Temperature, 
      .f = ~ mean(., na.rm = TRUE),
      .before = 1000,
      .after = 1000
    )
  )

elec_rolling

ggplot(elec_rolling) + 
  geom_line(aes(x = Date, y = Temperature), colour = "grey") + 
  geom_line(aes(x = Date, y = temp_moving_avg), colour = "red")

elec_rolling2 <- vic_elec %>% 
  mutate(
    temp_moving_avg = slide_dbl(
      .x = Temperature, 
      .f = ~ mean(., na.rm = TRUE),
      .before = 100,
      .after = 100,
      .complete = TRUE
    )
  )
elec_rolling2

ggplot(elec_rolling2) + 
  geom_line(aes(x = Date, y = Temperature), colour = "grey") + 
  geom_line(aes(x = Date, y = temp_moving_avg), colour = "red")

#feasts

key_vars(tourism)

[1] "Region"  "State"   "Purpose"

holidays <- tourism %>% 
  filter(Purpose %in% c('Holiday', 'business')) %>% 
  group_by(State) %>% 
  summarise(Trips = sum(Trips))

holidays

holidays %>% 
  autoplot(Trips)+
  xlab('Year')

holidays %>%
  filter(State %in% c("Queensland", "New South Wales", "Victoria")) %>%
  gg_season(Trips)

holidays %>%
  filter(State %in% c("Queensland", "New South Wales", "Victoria")) %>%
  gg_subseries(Trips)